Long-range autocorrelations of CpG islands in the human genome

Natural Selection and Scale Invariance

This review points out that three of the essential features of natural selection—competition for a finite resource, variation, and transmission of memory—occur in an extremely simple, thermalized molecular population, one of colliding “billiard balls” subject to an anisotropy, a directional flux of energetic molecules. The emergence of scaling behavior, scale invariance, in such systems is considered in the context of the emergence of complexity driven by Gibbs free energy, the origins of life, and known chemistries in planetary and astrophysical conditions. It is suggested that the thermodynamic formalism of statistical multifractality offers a parallel between the microscopic and macroscopic views of non-equilibrium systems and their evolution, different from, empirically determinable, and therefore complementing traditional definitions of entropy and its production in living systems. Further, the approach supports the existence of a bridge between microscopic and macroscopic scales, the missing mesoscopic scale. It is argued that natural selection consequently operates on all scales—whether or not life results will depend on both the initial and the evolving boundary conditions. That life alters the boundary conditions ensures nonlinearity and scale invariance. Evolution by natural selection will have taken place in Earth’s fluid envelope; both air and water display scale invariance and are far from chemical equilibrium, a complex condition driven by the Gibbs free energy arising from the entropy difference between the incoming solar beam and the outgoing infrared radiation to the cold sink of space acting on the initial conditions within evolving boundary conditions. Symmetry breaking’s role in the atmospheric state is discussed, particularly in regard to aerosol fission in the context of airborne bacteria and viruses in both current and prebiotic times. Over 4.4 billion years, the factors operating to support natural selection will have evolved along with the entire system from relative simplicity to the current complexity.

New framework of Getis-Ord's indexes associating spatial autocorrelation with interaction

Spatial autocorrelation and spatial interaction are two important analytical processes for geographical analyses. However, the internal relations between the two types of models have not been brought to light. This paper is devoted to integrating spatial autocorrelation analysis and spatial interaction analysis into a logic framework by means of Getis-Ord’s indexes. Based on mathematical derivation and transform, the spatial autocorrelation measurements of Getis-Ord’s indexes are reconstructed in a new and simple form. A finding is that the local Getis-Ord’s indexes of spatial autocorrelation are equivalent to the rescaled potential energy indexes of spatial interaction theory based on power-law distance decay. The normalized scatterplot is introduced into the spatial analysis based on Getis-Ord’s indexes, and the potential energy indexes are proposed as a complementary measurement. The global Getis-Ord’s index proved to be the weighted sum of the potential energy indexes and the direct sum of total potential energy. The empirical analysis of the system of Chinese cities are taken as an example to illustrate the effect of the improved methods and measurements. The mathematical framework newly derived from Getis-Ord’s work is helpful for further developing the methodology of geographical spatial modeling and quantitative analysis.

DNA methylome profiling beyond promoters - taking an epigenetic snapshot of the breast tumor microenvironment

Breast cancer, one of the most common and deadliest malignancies in developed countries, is a remarkably heterogeneous disease, which is clinically reflected by patients who display similar pathological features but respond differently to treatments. In the search for mediators of responsiveness, the tumor microenvironment (TME), in particular tumor-associated immune cells, has been pushed into the spotlight as it has become clear that the TME is an active component of breast cancer disease that affects clinical outcomes. Thus, the characterization of the TME in terms of cell identities and their frequencies has generated a great deal of interest. The common methods currently used for this purpose are either limited in accuracy or application, and DNA methylation has recently been proposed as an alternative approach. The aim of this review is to discuss DNA methylation profiling beyond promoters as a potential clinical tool for TME characterization and cell typing within tumors. With respect to this, we review the role of DNA methylation in breast cancer and cell-lineage specification, as well as inform about the composition and clinical relevance of the TME.